This is an interactive table of the covariate data.
The principal component analysis plot shown below was generated using the most varying 500 genes across all samples. The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design.
In presence of strong biological signal, the samples should cluster with the biological condition. When samples are clustered according to other effects (for example patient, or technical batch), great care must be used when interpreting the results, as the other effects will considerably reduce the ability to extract meaningful biological information.
The hierarchical clustering shown below was generated using the most varying 500 genes across all samples. The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.
The hierarchical clustering can provide clues on which groups of genes could affect the clustering of samples.
The hierarchical clustering shown below was generated using all the full normalised dataset (21264 genes). The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.
The expression values are obtained by the “vst” method, where the experimental design has been used for normalisation.
## Warning: Computation failed in `stat_binhex()`:
## Package `hexbin` required for `stat_binhex`.
## Please install and try again.
## Warning: Computation failed in `stat_binhex()`:
## Package `hexbin` required for `stat_binhex`.
## Please install and try again.
## Warning: Removed 62780 rows containing non-finite values (stat_boxplot).
## Warning: Removed 62780 rows containing non-finite values (stat_boxplot).
Contrasts generated by the pipeline.
A MA plot of the contrast NHBE_SC2V.
An interactive data table of the contrast results for NHBE_SC2V.
tmod enrichment analysis for NHBE_SC2VTable. Summary of the results for contrast NHBE_SC2V shows number of significant gene sets at various significance levels and for AUC > 0.65.
| DB | 0.01 | 0.001 | 1e-04 | 1e-06 |
|---|---|---|---|---|
| msigdb_reactome | 178 | 127 | 71 | 27 |
| msigdb_hallmark | 29 | 27 | 25 | 21 |
| msigdb_mir | 12 | 6 | 3 | 0 |
| msigdb_go_bp | 301 | 126 | 64 | 23 |
| tmod | 69 | 47 | 29 | 16 |
Table. Results of the tmod enrichment analysis for contrast NHBE_SC2V. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.
Dot plot for cluster profiler results for contrast NHBE_SC2V.
Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator
Enrichment map for cluster profiler results for contrast NHBE_SC2V.
Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…
UpSet plot for cluster profiler results for contrast NHBE_SC2V.
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
A MA plot of the contrast A549_SC2V.
An interactive data table of the contrast results for A549_SC2V.
tmod enrichment analysis for A549_SC2VTable. Summary of the results for contrast A549_SC2V shows number of significant gene sets at various significance levels and for AUC > 0.65.
| DB | 0.01 | 0.001 | 1e-04 | 1e-06 |
|---|---|---|---|---|
| msigdb_reactome | 172 | 96 | 53 | 25 |
| msigdb_hallmark | 18 | 18 | 18 | 14 |
| msigdb_mir | 18 | 12 | 3 | 1 |
| msigdb_go_bp | 240 | 143 | 83 | 35 |
| tmod | 46 | 29 | 22 | 10 |
Table. Results of the tmod enrichment analysis for contrast A549_SC2V. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.
Dot plot for cluster profiler results for contrast A549_SC2V.
Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator
Enrichment map for cluster profiler results for contrast A549_SC2V.
Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…
UpSet plot for cluster profiler results for contrast A549_SC2V.
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
A MA plot of the contrast A549_RSV.
An interactive data table of the contrast results for A549_RSV.
tmod enrichment analysis for A549_RSVTable. Summary of the results for contrast A549_RSV shows number of significant gene sets at various significance levels and for AUC > 0.65.
| DB | 0.01 | 0.001 | 1e-04 | 1e-06 |
|---|---|---|---|---|
| msigdb_reactome | 255 | 154 | 99 | 49 |
| msigdb_hallmark | 32 | 32 | 31 | 27 |
| msigdb_mir | 23 | 16 | 14 | 6 |
| msigdb_go_bp | 451 | 246 | 165 | 90 |
| tmod | 85 | 48 | 32 | 21 |
Table. Results of the tmod enrichment analysis for contrast A549_RSV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.
Dot plot for cluster profiler results for contrast A549_RSV.
Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator
Enrichment map for cluster profiler results for contrast A549_RSV.
Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…
UpSet plot for cluster profiler results for contrast A549_RSV.
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
A MA plot of the contrast A549_IAV.
An interactive data table of the contrast results for A549_IAV.
tmod enrichment analysis for A549_IAVTable. Summary of the results for contrast A549_IAV shows number of significant gene sets at various significance levels and for AUC > 0.65.
| DB | 0.01 | 0.001 | 1e-04 | 1e-06 |
|---|---|---|---|---|
| msigdb_reactome | 235 | 145 | 81 | 36 |
| msigdb_hallmark | 25 | 24 | 22 | 18 |
| msigdb_mir | 39 | 33 | 27 | 16 |
| msigdb_go_bp | 385 | 231 | 175 | 92 |
| tmod | 63 | 38 | 23 | 11 |
Table. Results of the tmod enrichment analysis for contrast A549_IAV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.
Dot plot for cluster profiler results for contrast A549_IAV.
Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator
Enrichment map for cluster profiler results for contrast A549_IAV.
Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…
UpSet plot for cluster profiler results for contrast A549_IAV.
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
A MA plot of the contrast A549_SC2V_vs_IAV.
An interactive data table of the contrast results for A549_SC2V_vs_IAV.
## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/
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tmod enrichment analysis for A549_SC2V_vs_IAVTable. Summary of the results for contrast A549_SC2V_vs_IAV shows number of significant gene sets at various significance levels and for AUC > 0.65.
| DB | 0.01 | 0.001 | 1e-04 | 1e-06 |
|---|---|---|---|---|
| msigdb_reactome | 197 | 100 | 66 | 23 |
| msigdb_hallmark | 22 | 20 | 20 | 18 |
| msigdb_mir | 83 | 72 | 63 | 45 |
| msigdb_go_bp | 430 | 239 | 172 | 88 |
| tmod | 75 | 50 | 22 | 6 |
Table. Results of the tmod enrichment analysis for contrast A549_SC2V_vs_IAV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.
Dot plot for cluster profiler results for contrast A549_SC2V_vs_IAV.
Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator
Enrichment map for cluster profiler results for contrast A549_SC2V_vs_IAV.
Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…
UpSet plot for cluster profiler results for contrast A549_SC2V_vs_IAV.
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
A MA plot of the contrast A549_SC2V_vs_RSV.
An interactive data table of the contrast results for A549_SC2V_vs_RSV.
## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/
## DT/server.html
tmod enrichment analysis for A549_SC2V_vs_RSVTable. Summary of the results for contrast A549_SC2V_vs_RSV shows number of significant gene sets at various significance levels and for AUC > 0.65.
| DB | 0.01 | 0.001 | 1e-04 | 1e-06 |
|---|---|---|---|---|
| msigdb_reactome | 342 | 230 | 147 | 81 |
| msigdb_hallmark | 32 | 31 | 29 | 27 |
| msigdb_mir | 54 | 47 | 38 | 24 |
| msigdb_go_bp | 679 | 439 | 300 | 188 |
| tmod | 116 | 79 | 56 | 29 |
Table. Results of the tmod enrichment analysis for contrast A549_SC2V_vs_RSV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.
Dot plot for cluster profiler results for contrast A549_SC2V_vs_RSV.
Enrichment map for cluster profiler results for contrast A549_SC2V_vs_RSV.
UpSet plot for cluster profiler results for contrast A549_SC2V_vs_RSV.
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’
Table. Overview of the databases for which gene set enrichment using tmod was performed.
| ID | Name | Description | TaxonID | N |
|---|---|---|---|---|
| msigdb_reactome | Reactome gene sets (MSigDB) | Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). | 9606 | 1499 |
| msigdb_hallmark | Hallmark gene sets (MSigDB) | Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). | 9606 | 50 |
| msigdb_mir | MIR targets (MSigDB) | MIR targets from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). | 9606 | 221 |
| msigdb_go_bp | GO Biological Process (MSigDB) | GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). | 9606 | 7350 |
| tmod | Co-expression gene sets (tmod) | Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information. | 9606 | 606 |
Database ID: msigdb_reactome.
Description: Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..
Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.
| Contrast | 0.05 | 0.01 | 0.001 | 1e-05 |
|---|---|---|---|---|
| NHBE_SC2V_ID0.pval | 453 | 299 | 204 | 84 |
| A549_SC2V_ID1.pval | 399 | 257 | 140 | 59 |
| A549_RSV_ID2.pval | 586 | 354 | 215 | 91 |
| A549_IAV_ID3.pval | 493 | 310 | 190 | 63 |
| A549_SC2V_vs_IAV_ID4.pval | 505 | 287 | 157 | 67 |
| A549_SC2V_vs_RSV_ID5.pval | 645 | 444 | 280 | 138 |
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf
Fig. Panel plot showing results for the database msigdb_reactome.
## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf
Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).
Database ID: msigdb_hallmark.
Description: Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..
Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.
| Contrast | 0.05 | 0.01 | 0.001 | 1e-05 |
|---|---|---|---|---|
| NHBE_SC2V_ID0.pval | 48 | 46 | 39 | 30 |
| A549_SC2V_ID1.pval | 41 | 37 | 35 | 25 |
| A549_RSV_ID2.pval | 46 | 44 | 41 | 36 |
| A549_IAV_ID3.pval | 42 | 37 | 32 | 20 |
| A549_SC2V_vs_IAV_ID4.pval | 46 | 44 | 38 | 29 |
| A549_SC2V_vs_RSV_ID5.pval | 46 | 45 | 42 | 35 |
Fig. Panel plot showing results for the database msigdb_hallmark.
Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).
Database ID: msigdb_mir.
Description: MIR targets from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..
Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.
| Contrast | 0.05 | 0.01 | 0.001 | 1e-05 |
|---|---|---|---|---|
| NHBE_SC2V_ID0.pval | 160 | 123 | 75 | 28 |
| A549_SC2V_ID1.pval | 172 | 138 | 89 | 32 |
| A549_RSV_ID2.pval | 177 | 150 | 93 | 51 |
| A549_IAV_ID3.pval | 178 | 145 | 108 | 49 |
| A549_SC2V_vs_IAV_ID4.pval | 191 | 164 | 133 | 84 |
| A549_SC2V_vs_RSV_ID5.pval | 196 | 151 | 109 | 71 |
Fig. Panel plot showing results for the database msigdb_mir.
Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).
Database ID: msigdb_go_bp.
Description: GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..
Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.
| Contrast | 0.05 | 0.01 | 0.001 | 1e-05 |
|---|---|---|---|---|
| NHBE_SC2V_ID0.pval | 2262 | 1434 | 913 | 510 |
| A549_SC2V_ID1.pval | 1434 | 887 | 583 | 297 |
| A549_RSV_ID2.pval | 2314 | 1461 | 962 | 569 |
| A549_IAV_ID3.pval | 1546 | 1013 | 653 | 350 |
| A549_SC2V_vs_IAV_ID4.pval | 1871 | 1212 | 793 | 440 |
| A549_SC2V_vs_RSV_ID5.pval | 2387 | 1588 | 1083 | 618 |
Fig. Panel plot showing results for the database msigdb_go_bp.
## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex
Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).
Database ID: tmod.
Description: Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information..
Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.
| Contrast | 0.05 | 0.01 | 0.001 | 1e-05 |
|---|---|---|---|---|
| NHBE_SC2V_ID0.pval | 159 | 95 | 62 | 21 |
| A549_SC2V_ID1.pval | 134 | 59 | 34 | 14 |
| A549_RSV_ID2.pval | 183 | 105 | 54 | 23 |
| A549_IAV_ID3.pval | 138 | 75 | 41 | 15 |
| A549_SC2V_vs_IAV_ID4.pval | 168 | 90 | 54 | 10 |
| A549_SC2V_vs_RSV_ID5.pval | 203 | 131 | 82 | 44 |
Fig. Panel plot showing results for the database tmod.
## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex
Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).
Table. Overview of the databases for which gene set enrichment using cluster_profiler was performed.
Fig. Panel plot showing results for the database MSigDb.H. Effect size is the normalized enrichment score (NES). Blue color indicates negative enrichment score, red color indicates positive NES. Size of the dots corresponds to the magnitude of NES as shown in the legend. Color intensity indicates p-value.
Fig. Panel plot showing results for the database MSigDb.C2. Effect size is the normalized enrichment score (NES). Blue color indicates negative enrichment score, red color indicates positive NES. Size of the dots corresponds to the magnitude of NES as shown in the legend. Color intensity indicates p-value.
Fig. Panel plot showing results for the database GO.BP. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.
Fig. Panel plot showing results for the database GO.MF. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.
## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex
Fig. Panel plot showing results for the database KEGG.pathways. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-conda_cos6-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /fast/work/users/jweiner_m/miniconda3/envs/sea_snap/lib/R/lib/libRlapack.so
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## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] orthomapper_0.0.0.9000 tmod_0.43 enrichplot_1.2.0 glue_1.3.1 pander_0.6.3
## [6] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3 readr_1.3.1
## [11] tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.3.0 magrittr_1.5
## [16] DT_0.11 yaml_2.2.0 DESeq2_1.22.1 SummarizedExperiment_1.12.0 DelayedArray_0.8.0
## [21] BiocParallel_1.16.6 matrixStats_0.55.0 Biobase_2.42.0 GenomicRanges_1.34.0 GenomeInfoDb_1.18.1
## [26] IRanges_2.16.0 S4Vectors_0.20.1 BiocGenerics_0.28.0 colorout_1.2-2
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5 fastmatch_1.1-0 Hmisc_4.2-0 plyr_1.8.4 igraph_1.2.4.2 lazyeval_0.2.2
## [8] splines_3.5.1 crosstalk_1.0.0 urltools_1.7.3 digest_0.6.23 plotwidgets_0.4 htmltools_0.4.0 GOSemSim_2.8.0
## [15] viridis_0.5.1 GO.db_3.7.0 checkmate_1.9.4 memoise_1.1.0 cluster_2.1.0 limma_3.38.3 graphlayouts_0.5.0
## [22] annotate_1.60.1 modelr_0.1.5 prettyunits_1.0.2 colorspace_1.4-1 ggrepel_0.8.1 blob_1.2.0 rvest_0.3.5
## [29] haven_2.2.0 xfun_0.11 tagcloud_0.6 crayon_1.3.4 RCurl_1.95-4.12 jsonlite_1.6 genefilter_1.64.0
## [36] zeallot_0.1.0 survival_2.44-1.1 polyclip_1.10-0 gtable_0.3.0 zlibbioc_1.28.0 XVector_0.22.0 UpSetR_1.4.0
## [43] DOSE_3.8.2 scales_1.1.0 pheatmap_1.0.12 vsn_3.50.0 DBI_1.0.0 Rcpp_1.0.3 progress_1.2.2
## [50] viridisLite_0.3.0 xtable_1.8-4 htmlTable_1.13.2 gridGraphics_0.4-1 europepmc_0.3 foreign_0.8-72 bit_1.1-14
## [57] preprocessCore_1.44.0 Formula_1.2-3 htmlwidgets_1.5.1 httr_1.4.1 fgsea_1.8.0 RColorBrewer_1.1-2 acepack_1.4.1
## [64] ellipsis_0.3.0 pkgconfig_2.0.3 XML_3.98-1.20 farver_2.0.1 nnet_7.3-12 dbplyr_1.4.2 locfit_1.5-9.1
## [71] reshape2_1.4.3 ggplotify_0.0.4 tidyselect_0.2.5 labeling_0.3 rlang_0.4.2 later_1.0.0 AnnotationDbi_1.44.0
## [78] munsell_0.5.0 cellranger_1.1.0 tools_3.5.1 cli_1.1.0 generics_0.0.2 RSQLite_2.1.2 ggridges_0.5.2
## [85] broom_0.5.3 evaluate_0.14 fastmap_1.0.1 knitr_1.26 bit64_0.9-7 fs_1.3.1 tidygraph_1.1.2
## [92] ggraph_2.0.0 nlme_3.1-141 mime_0.7 DO.db_2.9 xml2_1.2.2 compiler_3.5.1 rstudioapi_0.10
## [99] beeswarm_0.2.3 affyio_1.52.0 reprex_0.3.0 tweenr_1.0.1 geneplotter_1.60.0 stringi_1.4.3 lattice_0.20-38
## [106] Matrix_1.2-17 vctrs_0.2.0 pillar_1.4.2 lifecycle_0.1.0 BiocManager_1.30.10 triebeard_0.3.0 cowplot_1.0.0
## [113] data.table_1.11.6 bitops_1.0-6 qvalue_2.14.1 httpuv_1.5.2 R6_2.4.1 latticeExtra_0.6-28 affy_1.60.0
## [120] promises_1.1.0 gridExtra_2.3 MASS_7.3-51.4 assertthat_0.2.1 withr_2.1.2 GenomeInfoDbData_1.2.1 hms_0.5.2
## [127] grid_3.5.1 rpart_4.1-15 rmarkdown_2.0 rvcheck_0.1.7 Cairo_1.5-10 ggforce_0.3.1 shiny_1.4.0
## [134] lubridate_1.7.4 base64enc_0.1-3